Research on community evolution based on node influence and multi-attribute fusion

For the problem of low discrimination accuracy evolutionary events in dynamic social networks, a community evolution model (EMNI) based on node influence and multi-attribute fusion is proposed. Firstly, topological structure information nodes obtained by random walk local clustering coefficient, evaluated according to nodes. Secondly, order improve discriminating similarity, similarity method The EMNI combined characteristics stability difference, redefined seven events. Finally, effectiveness identifying verified different data sets. experimental results show that better than GED, PECT SGCI, which able identify more distribution also balanced.